A pose is understood in the technical sector to mean the spatial location of an object, namely the position and orientation of an object in a two-dimensional space or in a three-dimensional space.
The method for determining the pose of the vehicle is based at least additionally on landmarks of various types in the surroundings of the vehicle, whereby a pose basis may represent GPS data, for example. Pose data of the vehicle in this case based on GPS data may be augmented with data that are generated based on the recognition of landmarks. The orientation, for example, the driving direction of the vehicle, in particular, may be largely determined with the aid of landmarks. In this case, the accuracy of the determination of the pose of the vehicle based on landmarks is greater than the accuracy of the determination using GPS data. In semi-autonomously driving vehicles, in particular, in future, fully-autonomously driving vehicles, the pure GPS navigation for guiding the vehicle is no longer sufficient and new systems must be applied, which detect the immediate surroundings of the vehicle and assume the guidance of the vehicle, in particular, by recognizing landmarks. The term vehicle control system in this case includes essentially all components that are necessary for detecting the pose, for the evaluation of the data and, finally, for controlling the vehicle. The vehicle control system includes, in particular, detectors such as laser detectors, radar detectors, infrared sensors, capacitive sensors, LIDAR sensors and/or a video image capturing device.
For this purpose, German Published Patent Application No. 10 2014 206 901, for example, describes a method for determining the pose of an at least semi-autonomously driving vehicle in a surrounding area. The situation recognition in this method is based for one on a detection of the surroundings with the aid of a system of surroundings sensors, which includes ultrasonic sensors, laser sensors, radar sensors, infrared sensors and capacitive sensors, LIDAR sensors and/or a video image capturing device. The situation recognition in this case is intended to be based on the detection of objects outside the vehicle when the vehicle is moving in traffic, informers which also point to a particular situation being relevant. These may be, for example, visual markings, objects or boundaries. In addition or alternatively, additional technologies for localization may be used for improving the accuracy of the situation recognition, thus, geodata may be ascertained with the aid of a GPS system or digital maps with landmarks in combination with an odometry. Landmarks in this case are objects in the immediate surroundings of the vehicle, but also traffic signs, for example, such as traffic lights and the like, as well as roadway markings.
Thus, measuring data are used with the aid of detectors as a basis for sensing the surroundings of the vehicle, from which objects may be extracted with the aid of the detector algorithms. Based on these objects, it is possible to model the vehicle surroundings in order, for example, to consequently plan a trajectory for the host vehicle and to make other action decisions.
The quality of the surroundings model is largely a function of the system of surroundings sensors used. These differ in terms of the measuring properties with respect to accuracy and range depending on the system, and their efficiency is generally significantly a function of environmental conditions such as, for example, rain, fog, solar radiation or artificial illumination. For example, a roadway marking on a wet roadway, in particular, during darkness, may not reliably serve as a landmark, since a wet roadway, in particular, during darkness and in wet conditions may reflect, so that corresponding detectors may not be activated, however, other detectors continue to function in these weather conditions, for example, active light sources.
However, the influence factors do not affect all types of landmarks equally. Signal systems, for example, are generally easily detectable regardless of the weather conditions, whereas in the case of visually operating detection systems with actual objects in the immediate surroundings of the vehicle, for example, it is not possible to reliably model corresponding surroundings under all lighting conditions.
In addition to pieces of information based on sensor measurement data of the detectors on or in the vehicle, pieces of information from maps are increasingly used as an important additional source for the pose of the vehicle. These may be transmitted from a central map server or back-end server to the respective vehicle, referred to below simply as a back-end server. The pieces of surroundings information, which have been detected with the aid of the vehicle detectors, are then consolidated in the vehicle control system with the pieces of surroundings information, so that a mostly significantly higher quality for generating a surroundings model is achievable.
If all landmarks from the map are communicated with the aid of the back-end server to the vehicle control system, a significant information density is then created, which puts an unnecessary strain on the capacity of an available bandwidth of the communication channel between the back-end server and the vehicle control system of the vehicle. The computing capacity of the vehicle control system is also limited on the hardware side, so that it is desirable to reduce the amount of data transmitted by the back-end server to the vehicle control system and detector algorithms triggered as a result.